基于测距修正和改进BBA的三维DV-Hop算法

王佳豪; 江凌云

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基于测距修正和改进BBA的三维DV-Hop算法

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- 基于测距修正和改进BBA的三维DV-Hop算法
- A 3D DV-Hop Localization Algorithm Based on Distance Correction and Improved Binary Bat Algorithm
- 物联网学报 2026年
- 作者机构：
  
  南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
  
  江凌云，jiangly@njupt.edu.cn
- 基金信息：
  
  江苏省重点研发计划项目(BE2020084-4);国家自然科学基金资助项目;The National Natural Science Foundation of China(92067201)
- DOI：
  中图分类号： TN915.08
- 收稿：2025-04-12，
  
  修回：2025-05-31，
  
  录用：2025-06-10，
- 稿件说明：
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王佳豪, 江凌云. 基于测距修正和改进BBA的三维DV-Hop算法[J/OL]. 物联网学报, 2026.

Wang Jiahao, JIANG Lingyun. A 3D DV-Hop Localization Algorithm Based on Distance Correction and Improved Binary Bat Algorithm[J/OL]. Chinese Journal on Internet of Things, 2026.
王佳豪, 江凌云. 基于测距修正和改进BBA的三维DV-Hop算法[J/OL]. 物联网学报, 2026. DOI：

Wang Jiahao, JIANG Lingyun. A 3D DV-Hop Localization Algorithm Based on Distance Correction and Improved Binary Bat Algorithm[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

本文提出了一种基于测距修正和改进二进制蝙蝠算法（Binary Bat Algorithm

BBA）的三维DV-Hop定位算法（3D DV-Hop Localization Algorithm Based on Distance Correction and Improved Binary Bat Algorithm，3D IBBA-DV-Hop），旨在解决传统DV-Hop算法在复杂三维地形中由于地形遮挡、信号绕射和节点分布不均导致的定位误差。首先通过多通信半径结合接收信号强度指示（Received Signal Strength Indication，RSSI）优化了节点间最小跳数的估计误差。然后采用多项式平均方法提高锚节点平均跳距的估计精度。最后在坐标估计阶段，采用了改进的二进制蝙蝠算法（Improved Binary Bat Algorithm，BBA）筛选可靠锚节点集，进一步提高定位精度。仿真实验结果表明，在各向同性网络中3D IBBA-DV-Hop对比3D DV-Hop、3D IMSSA-DV-Hop和3D 3-Anchor-DV-Hop的平均定位误差分别降低了42.9%、20.3%、10.5%；在各向异性网络中，分别降低了53.1%、25.8%、14.4%。实验结果证明

本文提出的改进算法定位误差最小，并且该算法在各向异性网络中的定位效果更好。

Abstract

This paper presents a 3D DV-Hop localization algorithm based on distance correction and an improved Binary Bat Algorithm (BBA)

referred to as 3D IBBA-DV-Hop. The algorithm aims to address the localization errors of the traditional DV-Hop algorithm in complex 3D terrains due to terrain obstructions

signal diffraction

and uneven node distribution. First

the estimation error of the minimum hop count between nodes is optimized using a combination of multiple communication radii and Received Signal Strength Indication (RSSI). Then

the polynomial averaging method is employed to improve the estimation accuracy of the average hop distance of anchor nodes. Finally

during the coordinate estimation stage

the improved Binary Bat Algorithm (BBA) is used to select a reliable set of anchor nodes

further enhancing the localization accuracy. Simulation results show that

in an isotropic network

the 3D IBBA-DV-Hop reduces the average localization error by 42.9%

20.3%

and 10.5%

compared to 3D DV-Hop

3D IMSSA-DV-Hop

and 3D 3-Anchor-DV-Hop

respectively. In an anisotropic network

the reductions are 53.1%

25.8%

and 14.4%

respectively. The experimental results prove that the proposed improved algorithm minimizes localization errors and performs better in anisotropic networks.

关键词

Keywords

references

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